P16+ oropharyngeal squamous cell carcinoma (OP-SCC) affects a group of patients in which 15% to 35% of the patients suffer from recurrence, including metastasis. Nuclear morphology plays a key role in determining the grade and prognosis for a number of different cancers, including OP-SCC. Quantitative measurements of nuclear architecture and arrangement within local cell clusters represented on digitized histopathology images have been employed to differentiate between types of low and high risk cancers. Graph theory has been employed to study the spatial arrangement of nuclei and cells within digital pathology images. By considering a nucleus in a histology image as a vertex or node of a graph, and by connecting the vertices with edges, a variety of different spatial and nuclear graph arrangements may be quantified and tested for their value in predicting disease recurrence and outcome.
Conventional graph-based approaches to cancer grading and prediction compute first order statistics of inter-nuclear distance and nodal density and base their predictions on these first order statistics. For example, statistics related to edge length and node density extracted from spatial nuclear graphs, such as those derived from Voronoi triangulation and Delaunay triangulation, have been used to predict disease grade and severity. Other conventional graph-based approaches employ cell graphs to study local nuclear architectural complexity by decomposing an image into sub-graphs by replacing clusters of nuclei with a single representative node and encoding edge connections to proximal nodes only.
However, these conventional graph-based approaches do not capture the degree of complexity associated with repeating sub-graph patterns, and typically only extract global graph features. Most conventional graph-based approaches are limited to simple measurements such as mean/variance of inter-nuclear distance or measurements related to node density. These first order statistics do not capture the degree of complexity associated with repeating sub-graph patterns. Furthermore, global graph edges often traverse the epithelial and stroma regions, which have distinct biological morphology in tumor progression. Moreover, since conventional graph-based approaches inherently extract only global features, important information involving local spatial interactions is left unexplored. Thus, conventional approaches to predicting cancer recurrence are not optimal, and an improved approach to predicting cancer recurrence or progression is desirable.